benchmarking: added gpu evaluators to performance testing. getting execution errors still

package/src/Interpreter.jl

@@ -12,19 +12,25 @@ export interpret
  - variables::Matrix{Float32} : The variables to use. Each column is mapped to the variables x1..xn
  - parameters::Vector{Vector{Float32}} : The parameters to use. Each Vector contains the values for the parameters p1..pn. The number of parameters can be different for every expression
 "
-function interpret(expressions::Vector{ExpressionProcessing.PostfixType}, variables::Matrix{Float32}, parameters::Vector{Vector{Float32}})::Matrix{Float32}
+function interpret(expressions::Vector{Expr}, variables::Matrix{Float32}, parameters::Vector{Vector{Float32}})::Matrix{Float32}
+	
+	exprs = Vector{ExpressionProcessing.PostfixType}(undef, length(expressions))
+	for i in eachindex(expressions)
+		exprs[i] = ExpressionProcessing.expr_to_postfix(expressions[i])
+	end
+	
 	variableCols = size(variables, 2) # number of variable sets to use for each expression
 	cudaVars = CuArray(variables)
 	cudaParams = Utils.create_cuda_array(parameters, NaN32) # column corresponds to data for one expression
-	cudaExprs = Utils.create_cuda_array(expressions, ExpressionElement(EMPTY, 0)) # column corresponds to data for one expression
+	cudaExprs = Utils.create_cuda_array(exprs, ExpressionElement(EMPTY, 0)) # column corresponds to data for one expression
 	# put into seperate cuArray, as this is static and would be inefficient to send seperatly to every kernel
-	cudaStepsize = CuArray([Utils.get_max_inner_length(expressions), Utils.get_max_inner_length(parameters), size(variables, 1)]) # max num of values per expression; max nam of parameters per expression; number of variables per expression
+	cudaStepsize = CuArray([Utils.get_max_inner_length(exprs), Utils.get_max_inner_length(parameters), size(variables, 1)]) # max num of values per expression; max nam of parameters per expression; number of variables per expression
 
 	# each expression has nr. of variable sets (nr. of columns of the variables) results and there are n expressions
-	cudaResults = CuArray{Float32}(undef, variableCols, length(expressions))
+	cudaResults = CuArray{Float32}(undef, variableCols, length(exprs))
 
 	# Start kernel for each expression to ensure that no warp is working on different expressions
-	for i in eachindex(expressions)
+	for i in eachindex(exprs)
 		kernel = @cuda launch=false interpret_expression(cudaExprs, cudaVars, cudaParams, cudaResults, cudaStepsize, i)
 		config = launch_configuration(kernel.fun)
 		threads = min(variableCols, config.threads)